Osteoarthritis imaging最新文献

{"title":"OSTEOARTHRITIS AND CHRONIC BACK PAIN ARE ASSOCIATED WITH LATERAL SPINE SHAPE: A STUDY USING THE UK BIOBANK","authors":"F.R. Saunders ,&nbsp;J. Parkinson ,&nbsp;R.M. Aspden ,&nbsp;T. Cootes ,&nbsp;J.S. Gregory","doi":"10.1016/j.ostima.2025.100315","DOIUrl":"10.1016/j.ostima.2025.100315","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Chronic back pain is very common and affects over 600 million adults worldwide and has been partly attributed to OA. We have previously shown that the lateral spine has an intrinsic shape and that specific shapes have been shown to be associated with back pain in early old age. However, there is little evidence in the literature that directly links lateral spine shape with OA.</div></div><div><h3>OBJECTIVE</h3><div>To explore the relationships between OA, chronic back pain and lateral spine shape in a sub-cohort of the UK Biobank.</div></div><div><h3>METHODS</h3><div>Lateral spine iDXA scans (n=4784) from the UK Biobank imaging enhancement study were used. The cohort was 52.1% female, and the mean age was 62.2±7.5 years (Table 1). Images were annotated semi-automatically using a 143-point template encompassing the vertebral bodies from T7 to the superior margin of L5 using custom software (The University of Manchester). The points were subjected to Procrustes transform and then Principal Component Analysis to build a statistical shape model (SSM). Self-reported OA and chronic back pain (greater than 3 months duration) were taken from the questionnaire data provided at the imaging centre visit. Binary logistic regression was used to explore the associations between self-reported OA, chronic back pain, and the first 10 modes of variation. The model was adjusted for age, sex, height, weight and total spine BMD. We report odds ratios (OR) with 95% confidence intervals (CI) for each standard deviation change in mode.</div></div><div><h3>RESULTS</h3><div>537 participants reported OA (not site specific) and 630 reported chronic back pain. The first 10 SSM modes accounted for 88.9% of the total model variation. We found that three modes were associated with self-reported OA (modes 3,9 &amp; 10) and a single mode was associated with chronic back pain (mode 3). It was observed that mode 3 (6.5% total model variation; Fig 1.), describing vertebral height and decreased vertebral column height was negatively associated with both self-reported OA [OR 0.88 95% CI 0.8-0.97, p=0.007] and chronic back pain [OR 0.81 95% CI 0.70-0.94, p=0.005]. Mode 3 also described a loss of spinal curvature (Fig. 1). Mode 9 (0.7% of total model variation), describing narrowing of the lumbar vertebrae) and mode 10 (0.5% of total model variation), describing a disconnect between lumbar and thoracic sections of the vertebral column were associated with an increased risk of OA [mode 9 OR 1.11 95% CI 1.01-1.022, p=0.031; mode 10 OR 1.12 95% CI 1.02-1.23, p=0.011].</div></div><div><h3>CONCLUSION</h3><div>We found that loss of spinal curvature and decreased vertebral body height were negatively associated with OA. Our data indicated that there was an increased risk of OA with rotation of the spine.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100315"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GENETIC SULFATE WASTING, A MONOGENIC CAUSE OF SEVERE INTERVERTEBRAL DISC HEIGHT LOSS","authors":"J.M. Hou ,&nbsp;D.D.G. Chappell ,&nbsp;E. Gkrania-Klotsas ,&nbsp;S.M. Park ,&nbsp;P. Freeman ,&nbsp;M. Duer ,&nbsp;K.E.S. Poole","doi":"10.1016/j.ostima.2025.100293","DOIUrl":"10.1016/j.ostima.2025.100293","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Loss of function SLC13A1 variants cause failure to reabsorb sulfate in proximal tubule, reduced serum sulfate, and intervertebral disc disease via glycosaminoglycan abnormalities. A rheumatology patient was found to be homozygous for SCL13A1, explaining an extraordinary spinal disc loss phenotype.</div></div><div><h3>OBJECTIVE</h3><div>To define intervertebral disc heights and measure sulfate levels and excretion.</div></div><div><h3>METHODS</h3><div>The homozygote is a 45-year-old female who was only 14 when her already severe degenerative disc disease necessitated her first lumbar laminectomy, with a second performed 4 years later. Her lumbar and thoracic range of motion is greatly reduced. She suffers from severe back pain. Whole genome sequencing identified a stop-gain variant on chromosome 7 at ex.2 c.34C&gt;T p. (Arg12Ter). We measured her radiographic intervertebral disc heights to compare with matched reference values from other studies and older controls from previous Cambridge studies, with 3D reconstructions given the extraordinary disc loss phenotype. The homozygote has a decreased plasma sulfate compared to reference values (149 vs225-494μmol/l). Her urine sulfate is high at 2086umol/l for plasma level. Sulfate excretion rate is excessive 1605mmol/mol creatinine (ref. 444-5431mmol/mol)</div></div><div><h3>RESULTS</h3><div>Radiographic measurements showed widespread loss of disc height. The proband's brother is also under our care for multiple musculoskeletal (MSK) problems; he is heterozygous for SLC13A1 but has normal disc heights.</div></div><div><h3>CONCLUSIONS</h3><div>How renal sulphate wasting results in intervertebral disc degeneration in SLC13A1 homozygotes is unclear. Studying such patients might provide an avenue for therapeutic intervention to target widespread disc disease.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100293"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EXPLORING THE RELATIONSHIP BETWEEN LIGAMENT MICROSTRUCTURE AND MECHANICS IN OA-AFFECTED HUMAN KNEES","authors":"A. Gheisari ,&nbsp;A. Kositsky ,&nbsp;V.-P. Karjalainen ,&nbsp;S. Das Gupta ,&nbsp;V. Virtanen ,&nbsp;E. Nippolainen ,&nbsp;H. Kröger ,&nbsp;J. Töyräs ,&nbsp;S. Saarakkala ,&nbsp;I.O. Afara ,&nbsp;R.K. Korhonen ,&nbsp;M.A.J. Finnilä","doi":"10.1016/j.ostima.2025.100287","DOIUrl":"10.1016/j.ostima.2025.100287","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Knee ligaments play a critical role in providing joint stability and limiting excessive motion. Altered joint loading due to OA can affect various knee tissues, including the ligaments. Previous studies using post-traumatic OA animal models have reported changes in affected knee’s ligament viscoelasticity. This study investigates the microstructural characteristics of OA-affected human knee ligaments and examines whether these features are related to their viscoelastic properties.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study examines whether clustered bundle thickness and the proportion of non-collagenous volume to total ligament volume differs among ligaments in OA-affected knees. It also examines if the mechanical properties of the ligaments are dependent on the bundle thickness and proportion of non-collagenous volume.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Anterior (ACL; n = 6) and posterior (PCL; n = 7) cruciate ligaments, and medial (MCL; n = 8) and lateral (LCL; n = 7) collateral ligaments were collected from eight fresh-frozen cadaveric knees (five female; age: 65 ± 8 years). All knees had histology-confirmed osteoarthritis (average OARSI grade of tibial cartilage samples: &gt;2). Following preconditioning, samples underwent a mechanical testing protocol that included a two-step stress relaxation (to 4% and 8% strain, 30 min each) and cyclic loading up to 5.0 Hz with ±0.5% strain amplitude and 20 cycles per frequency. Equilibrium modulus was derived from the stress-relaxation data, while dynamic modulus and phase difference were calculated from cyclic loading. After mechanical testing, samples were stored in formalin and underwent gradual dehydration in ethanol and critical point drying. Subsequently, they were imaged by an Xradia 610 Versa X-ray microscopy (XRM, with 4x objective, 40kV voltage, 2s exposure, 10µm voxel size, and binning of 4). The reconstructed XRM images of ligaments were visualized in CTVox, and collagen bundle thickness and non-collagenous volume (by open and close porosity analyses) were calculated in CTAn software. Pearson correlation analysis between collagen bundle thickness and non-collagenous volume and mechanical properties was performed in R 4.2.2.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Among the four ligaments, the LCL exhibited the highest bundle thickness, while the PCL showed the highest non-collagenous volume ratio; however, these differences were not statistically significant. Equilibrium modulus was negatively correlated with bundle thickness across all ligaments, and with non-collagenous volume in all but the MCL. The phase difference at 5 Hz in the PCL showed a strong positive correlation with bundle thickness (&lt;em&gt;r&lt;/em&gt; = 0.82). The ACL displayed a strong negative correlation between dynamic modulus at 5 Hz and non-collagenous volume (&lt;em&gt;r&lt;/em&gt; = –0.82).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;Although collagen is the primary load-bearing component of ligaments, incre","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100287"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STATISTICAL SHAPE MODELING OF COMPUTED TOMOGRAPHY-DERIVED CARPAL BONES REFLECTS SCAPHOLUNATE INTEROSSEOUS LIGAMENT INJURY","authors":"T.P. Trentadue ,&nbsp;A.R. Thoreson ,&nbsp;K.D. Zhao","doi":"10.1016/j.ostima.2025.100324","DOIUrl":"10.1016/j.ostima.2025.100324","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Injuries to the scapholunate interosseous ligament (SLIL) are among the most common upper extremity injuries. Early, accurate diagnosis is essential to minimize progression of scapholunate advanced collapse (SLAC)-pattern radiocarpal OA &lt;sup&gt;1&lt;/sup&gt;. SLIL injuries widen the SL interval and contribute to the scaphoid palmar flexion and lunate extension of dorsal intercalated segment instability &lt;sup&gt;2&lt;/sup&gt;. Radiographs are limited by carpal overlap and sensitivity to forearm pronosupination angle &lt;sup&gt;3&lt;/sup&gt;. Volumetric imaging-derived three-dimensional (3D) bone models can be used in statistical shape modeling (SSM) to compare joint alignment and morphology, mitigating challenges of planar imaging.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;The objective of this study is to compare 3D carpal alignment in wrists with and without SLIL injury using a multi-level (shape and alignment), multi-object (three bone) (MLMO) SSM. We hypothesize that (1) there will be differences in the 3D morphology of the radius, scaphoid, and lunate between wrists with versus without SLIL injury and (2) these differences will affect joint space width.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Twenty-one participants (14.3% female, median [25&lt;sup&gt;th&lt;/sup&gt;-75&lt;sup&gt;th&lt;/sup&gt; percentile] age 42.0 [26.8-50.0] years, 57.1% dominant hand injury) with arthroscopically-confirmed, unilateral SLIL injuries were recruited to a prospective clinical trial evaluating the role of CT in detecting SLIL injuries &lt;sup&gt;4&lt;/sup&gt;. Bilateral wrist CT images were acquired (SOMATOM Force and NAEOTOM Alpha, Siemens Healthineers, Germany) using published acquisition parameters &lt;sup&gt;4&lt;/sup&gt;. The radius, scaphoid, and lunate were segmented from static CT with semi-automated algorithms (Analyze Pro, Mayo Foundation for Medical Education and Research, Rochester, MN). Segmentation maps were used to generate 3D stereolithography meshes of each bone. Left-handed images were reflected to right-handed anatomies. MLMO SSM was performed (ShapeWorks v6.3.2 &lt;sup&gt;5&lt;/sup&gt;) &lt;sup&gt;6&lt;/sup&gt;. Linear discriminant analysis (LDA), a form of supervised machine learning for dimensionality reduction and class separation, was used to compare uninjured and injured morphologies &lt;sup&gt;7&lt;/sup&gt;. Discriminant scores between wrists were compared with a Wilcoxon signed rank test. SSM-derived bone surface particles of the mean uninjured and mean injured bones were used to calculate interosseous proximities, a metric approximating joint space width, at the SL interval and radioscaphoid joint using &lt;em&gt;k-&lt;/em&gt;nearest neighbor methods within distance thresholds of 5.0 mm and 2.5 mm, respectively &lt;sup&gt;8,9&lt;/sup&gt;. Interosseous proximity distributions were compared using two-sided Kolmogorov-Smirnov (KS2) tests. Significance was defined as α=0.05 with Bonferroni corrections as appropriate.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;There was a significant difference in LDA joint shape and alignment discriminant scores between un","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100324"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEEP LEARNING MODELS FOR AUTOMATIC JOINT SPACE WIDTH MEASUREMENT","authors":"Z. Wang ,&nbsp;J. Crawmer ,&nbsp;A. Guermazi ,&nbsp;J. Duryea ,&nbsp;M. Jarraya","doi":"10.1016/j.ostima.2025.100355","DOIUrl":"10.1016/j.ostima.2025.100355","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Accurate and automated measurement of femorotibial JSW (fJSW) is crucial for assessing and monitoring OA. Current semi-automated (SA) fJSW measurement methods can be time-consuming and prone to inter-observer variability. This work describes the evaluation of a deep learning (DL) approach to substantially automate fJSW measurement from knee radiographs.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate the performance of a DL method for automatic fJSW measurement by comparing it to a standard SA method.</div></div><div><h3>METHODS</h3><div>We randomly selected a single knee radiograph from 295 OAI participants (49 knees for each KL grade 0-4) that were not used for DL training. We measured the BL and 48mo. medial fixed-location fJSW at x=0.25 using both the SA and DL methods. fJSW(x=0.25) have been shown to be the most responsive location compared to other fJSW locations and minimum JSW. The SA fJSW measurement consists of a first step to delineate the femur for setting up the necessary coordinate system, followed by a second step to delineate the femur and tibia for measuring fJSW. We trained separate DL algorithms for each step. The models employed an Attention U-Net architecture for segmenting joint spaces. This network enhances the standard U-Net encoder-decoder structure with attention mechanisms. The U-Net's encoder path progressively captures contextual information through a series of convolutional and pooling layers. The decoder path then gradually reconstructs the segmentation map by up-sampling features and combining them with high-resolution features from the encoder via skip connection. To assess performance, we calculated failure rates (assessed visually) for each step, the fJSW_DL to fJSW_SA correlation (Pearson’s R), and the responsiveness (standardized response mean: SRM). For DL coordinate system failures, the reader made manual corrections so all knees could be passed to the DL fJSW algorithm.</div></div><div><h3>RESULTS</h3><div>There were 58 coordinate systems failures (11.7%) with a KL distribution as follows: KL0:2, KL1:7, KL2:4, KL3:9, KL4:36, and 31 fJSW (6.2%) failures distributed as follows: KL0:4, KL1:1, KL2:4, KL3:7, KL4:15. We excluded the JSW failures leaving knees from 215 participants for the correlation and responsiveness analyses. The Pearson’s correlation was R = 0.97 and the SRM values were -0.64 (SA) and -0.67 (DL). Figure 1 is a Bland-Altman plot comparing the SA and DL fJSW, showing a minor bias and few outliers.</div></div><div><h3>CONCLUSION</h3><div>The results demonstrate that a DL algorithm can measure fJSW accurately with equivalent or better responsiveness compared to the SA method, dramatically reducing the reader time while maintaining performance. The majority of the failures were for KL4 knees, which are less utilized for KOA studies. The DL software has the potential to be used in very large studies and clinical trials of KOA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100355"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STUDY POPULATION SELECTION USING MACHINE LEARNING FROM THE FNIH BIOMARKERS CONSORTIUM PROGRESS OA COHORT","authors":"E.B. Dam ,&nbsp;J. Collins ,&nbsp;F. Eckstein ,&nbsp;F.W. Roemer ,&nbsp;A. Guermazi ,&nbsp;D.J. Hunter","doi":"10.1016/j.ostima.2025.100283","DOIUrl":"10.1016/j.ostima.2025.100283","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Stringent participant selection criteria for DMOAD trials are crucial, but there is no consensus on the criteria currently accepted by the regulatory authorities, particularly not criteria adapted to different treatment targets. Ensuring a population with a higher probability of treatment-specific OA progression may facilitate cost-effective trials with less risk of failure.</div></div><div><h3>OBJECTIVE</h3><div>To investigate whether simple Machine Learning (ML) methods provide more effective and/or transparent selection criteria than conventional statistical methods.</div></div><div><h3>METHODS</h3><div>We investigated the FNIH Biomarkers Consortium cohorts. Phase 1 included 600 subjects from the OAI, as a case/control cohort wrt. OA progression defined by JSW and/or pain progression (JSN decrease ³ 0.7 mm, WOMAC total pain increase ³ 9). Phase 2 included control groups from DMOAD trials (SEKOIA, VIDEO, ILLUSTRATE-K, ROCCELLA), in total 1233 subjects, using the same JSW/Pain endpoints. Consortium members provided biomarker scores for potentially prognostic biomarkers. We focused on the baseline imaging biomarkers submitted for both phases, including semi-quantitative MOAKS readings and quantitative cartilage morphology from MRI. We analyzed the sub-cohorts with complete imaging and clinical biomarkers, i.e. 600 and 366 subjects, respectively. We used a k-nearest neighbor classifier to predict progression as defined by the endpoints selecting a biomarker subset using sequential forward feature selection (SFFS). Model training and validation were performed using 10-fold cross-validation (CV). We measured model performance by the median AUC score across the CV test sets. The performance was compared to a classical logistic regression model with elastic-net regularization, which was trained and scored using the same SFFS and CV.</div></div><div><h3>RESULTS</h3><div>The AUC scores and selected imaging biomarkers for each progression endpoints are shown in Table 1 below. In general, compared to the Logistic Regression models, the kNN models performed on par or slightly better (Phase 1: 0.80 vs 0.79 for JSN and 0.66 vs 0.68 for Pain; Phase 2: 0.76 vs 0.77 for JSN and 0.82 vs 0.73 for Pain).</div></div><div><h3>CONCLUSION</h3><div>The ML model possibly performed slightly better than classical logistic regression. However, it should be analyzed whether the two models include the same biomarkers and what the implications are for the required study sample size. Further, the feature selection step is very relevant for clinical trial design, to ensure a limited yet predictive set of features.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100283"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Committee Members","authors":"","doi":"10.1016/S2772-6541(25)00103-5","DOIUrl":"10.1016/S2772-6541(25)00103-5","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100363"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sponsors","authors":"","doi":"10.1016/S2772-6541(25)00106-0","DOIUrl":"10.1016/S2772-6541(25)00106-0","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100366"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLINICAL TRANSLATION PIPELINE FOR DETECTING SENESCENCE IN OSTEOARTHRITIS USING THE Β-GALACTOSIDASE RESPONSIVE GD-CHELATE","authors":"V. Suryadevara ,&nbsp;R. von Kruechten ,&nbsp;J.H. Tang ,&nbsp;A.M. Dreisbach ,&nbsp;Z. Shokri Varniab ,&nbsp;S.B. Singh ,&nbsp;A. Lubke ,&nbsp;T. Liang ,&nbsp;J. Wong ,&nbsp;J. Wang ,&nbsp;R. Duwa ,&nbsp;J. Wang ,&nbsp;M. Barbieri ,&nbsp;F. Kogan ,&nbsp;S.B. Goodman ,&nbsp;L. Chou ,&nbsp;D. Oji ,&nbsp;J. Chan ,&nbsp;T.J. Meade ,&nbsp;H.E. Daldrup-Link","doi":"10.1016/j.ostima.2025.100335","DOIUrl":"10.1016/j.ostima.2025.100335","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Cellular senescence is one of the key mechanisms implicated in the development and progression of OA. The identification of senescence-mediated molecular mechanisms in OA needs novel imaging tools to detect senescence and monitor the efficacy of new senolytic therapies. Progress in molecular imaging techniques has led to the creation of a novel β-gal responsive Gd-chelate for identifying senescence using MRI, the widely used imaging modality for OA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;The hypothesis is that β-gal responsive Gd-chelate can detect senescence &lt;em&gt;in vitro, in vivo&lt;/em&gt; and in human OA specimens.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Senescence was induced in mesenchymal stem cells (MSCs) using 400nM doxorubicin over 5 days. Control and senescent cell suspensions incubated with 0.25 mM β-gal responsive Gd-chelate underwent MRI on a 3T MRI scanner (Bruker BioSpec, Billerica, MA). Further cartilage defects created in pig knees were implanted with control and senescent cells, followed by MRI after intraarticular injection of 2.5 mM β-gal responsive Gd-chelate. As a first step of clinical translation, human OA specimens were obtained from 30 patients undergoing hip/knee/ankle replacement. The fresh specimens were incubated with 2.5 mM of β-gal responsive Gd-chelate for an hour, before and after which MRI was performed using the following parameters: fat-saturated PD-weighted fast spin-echo sequence (TR=1500ms, matrix size=512 × 512pixels, slice thickness(=1mm, FOV=15cm, and NEX=2); SMART1 MAP sequence (TR = 40, 75, 150, 300, 500, 700, and 2,000 ms, matrix size=160 × 160 pixels SL=6mm, FOV=15 cm, and NEX=1) and T&lt;sub&gt;1&lt;/sub&gt; weighted fast SE sequence (TR=500ms, matrix size=512 × 512 pixels, SL=1mm, FOV=15cm, and NEX=2). T1 maps were generated to calculate the T1 relaxation times.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Senescence was first confirmed with immunohistochemistry for senescence markers including p16, p21 and β-gal. &lt;em&gt;In vitro&lt;/em&gt; studies indicated that senescent MSCs demonstrated a notable increase in MRI signal after being incubated with the β-gal responsive Gd-chelate probe, compared to control cells (Fig. 1A). &lt;em&gt;In vivo&lt;/em&gt;, the probe was injected intraarticularly into pig knee joints, and a marked decrease in T1 relaxation times indicated the retention of the probe and it’s activation by senescent cells in cartilage defects (Fig. 1B). The Wilcoxon ranksum test was used to determine the significance between control and senescence group. In human OA specimens, areas with severe cartilage damage as graded by a radiologist using Outerbridge score demonstrated higher number of senescent cells seen on immunohistochemistry. MRI indicated that there is pronounced hyperintense signal in the T1-SE images upon incubation with the β-gal responsive Gd-chelate probe, compared to MRI of the specimens before incubation. This was further quantified on T1 maps and indicated a significant reduction in T&lt;su","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100335"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SPONTANEOUS CARTILAGE THICKENING IN OSTEOARTHRITIS KNEES: DATA FROM IMI-APPROACH AND THE OAI","authors":"C. Salzlechner ,&nbsp;W. Wirth ,&nbsp;S.C. Mastbergen ,&nbsp;M. Kloppenburg ,&nbsp;F.J. Blanco ,&nbsp;I.K. Haugen ,&nbsp;F. Berenbaum ,&nbsp;M.P. Jansen","doi":"10.1016/j.ostima.2025.100313","DOIUrl":"10.1016/j.ostima.2025.100313","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Articular cartilage was thought to have minimal repair capacity, but treatments like knee joint distraction show that regeneration is possible. Preliminary analyses have also suggested the possibility of spontaneous thickening of cartilage: thickening without external regenerative intervention.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to evaluate spontaneous thickening in osteoarthritic knees.</div></div><div><h3>METHODS</h3><div>Patients from IMI-APPROACH and OAI cohorts were included. MRI-based mean medial and lateral cartilage thickness (ThCtAB; Chondrometrics) and knee radiographs were obtained at baseline, 1-year, and 2-year follow-up. Minimum medial and lateral joint space width (mJSW) and Kellgren-Lawrence grade (KLG) were automatically assessed from radiographs using KOALA (ImageBiopsy Lab). For each knee, mean whole-joint mJSW and ThCtAB changes over 2 years were calculated using linear regression. Knees were categorized as ‘thickening’ if both the mJSW and ThCtAB change were positive and as ‘thinning’ if both were negative; knees with inconsistent results were excluded. This approach was chosen because an increase in mJSW may reflect joint wedging or positional changes and increased ThCtAB may indicate swelling; especially in these expectedly relatively small changes, only an increase in both likely reflects true structural thickening.</div><div>Patient characteristics and two-year changes were compared using Mann-Whitney U and chi-square tests.</div></div><div><h3>RESULTS</h3><div>Out of 1,457 knees analyzed, 203 (14%) demonstrated thickening and 658 (45%) thinning. Patients with thickening were younger, predominantly female, had less pain and a lower KLG compared to those with thinning (all p&lt;0.05). Over 2 years, knees with thickening received significantly fewer injections (p=0.043) and showed greater improvements in both mJSW and ThCtAB (both p&lt;0.001). Complete results are presented in Table 1.</div></div><div><h3>CONCLUSION</h3><div>Spontaneous cartilage thickening can occur in osteoarthritic knees and is more prevalent in younger females with less severe joint damage. Future research is needed to determine whether this thickening (repair) potential can be predicted and may guide regenerative treatment options.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100313"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}